Inverse Signal Classification for Financial Instruments

Uri Kartoun

Papers from arXiv.org

Abstract: The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system using a collection of 7,881 financial instruments traded during 2011 to identify inverse behavior among the time-series.

Date: 2013-02, Revised 2013-03
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1303.0283

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